A Bayesian Approach for Inferring Sea Ice Loads

TL;DR

This paper introduces a Bayesian inference framework to estimate sea ice loads using strain gauges on buoys, enhancing in situ measurement capabilities for Arctic ice stress analysis amid climate change.

Contribution

It proposes a novel Bayesian measurement framework that utilizes existing buoy platforms with strain gauges to infer internal ice stresses from limited data.

Findings

Successful experimental validation with simulated ice conditions

Linking strain measurements to applied loads via finite element modeling

Potential for improved in situ Arctic ice stress monitoring

Abstract

The Earth's climate is rapidly changing and some of the most drastic changes can be seen in the Arctic, where sea ice extent has diminished considerably in recent years. As the Arctic climate continues to change, gathering in situ sea ice measurements is increasingly important for understanding the complex evolution of the Arctic ice pack. To date, observations of ice stresses in the Arctic have been spatially and temporally sparse. We propose a measurement framework that would instrument existing sea ice buoys with strain gauges. This measurement framework uses a Bayesian inference approach to infer ice loads acting on the buoy from a set of strain gauge measurements. To test our framework, strain measurements were collected from an experiment where a buoy was frozen into ice that was subsequently compressed to simulate convergent sea ice conditions. A linear elastic finite element model was used to describe the response of the deformable buoy to mechanical loading, allowing us to link the observed strain on the buoy interior to the applied load on the buoy exterior. The approach presented in this paper presents an instrumentation framework that could use existing buoy platforms as in situ sensors of internal stresses in the ice pack.